Bayesian ridge estimation of age-period-cohort models
Bayesian ridge estimation of age-period-cohort models
复制标题
年龄-周期-队列模型的贝叶斯岭估计
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
D. Powers
中科院分区：
文献类型：
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作者：
Minle Xu;D. Powers
同期队列（APC）分析提供了一个框架，研究趋势的三个时间维度的基础年龄按时期表。然而，年龄、时期和队列之间的完美线性关系导致了一个众所周知的识别问题，因为身份队列=时期-年龄的完美共线性。已经提出了许多方法来处理这个识别问题，例如，内在估计（IE），它可以被看作是岭回归的一种限制形式。贝叶斯回归提供了一种替代方法来建模表格年龄，时期，队列数据。本研究认为，从贝叶斯的角度来看，通过引入先验分布的岭参数，这使得这些参数估计与实质性的参数，而不是被分配（和固定）先验。结果表明，一个贝叶斯岭模型与一个共同的先验的岭参数产生估计的年龄，时期和队列效应类似的内在估计的基础上，和那些基于传统的岭估计的收缩惩罚从交叉验证。然而，具有不同先验的年龄、时期和队列效应的岭参数的贝叶斯模型的性能受先验分布的选择的影响。因此，需要进一步研究先验分布选择的影响。
Age-Period-Cohort (APC) analysis offers a framework to study trends in the three temporal dimensions underlying age by period tables. However, the perfect linear relationship among age, period, and cohort leads to a well-known identification issue due perfect colinearity from the identity Cohort = Period − Age. A number of methods have been proposed to deal with this identification issue, e.g., the intrinsic estimator (IE), which may be viewed as a limiting form of ridge regression. Bayesian regression offers an alternative approach to modeling tabular age, period, cohort data. This study views the ridge estimator from a Bayesian perspective by introducing prior distributions for the ridge parameters, which permits these parameters to be estimated jointly with the substantive parameters rather than being assigned (and fixed) a-priori. Results show that a Bayesian ridge model with a common prior for the ridge parameter yields estimated age, period, and cohort effects similar to those based on the intrinsic estimator and to those based on a conventional ridge estimator with a shrinkage penalty obtained from cross-validation. The performance of Bayesian models with distinctive priors for the ridge parameters of age, period, and cohort effects is, however, affected by the choice of prior distributions. Further investigation of the influence of the choice of prior distributions is therefore warranted.
影响因子：
5.4
作者：
Keyes, Katherine M.;Utz, Rebecca L.;Robinson, Whitney;Li, Guohua
通讯作者：
Li, Guohua
子宫颈腺癌的持续增加：出生队列现象。
影响因子：
7.7
作者：
Zheng,T;Holford,TR;Ma,Z;Chen,Y;Liu,W;Ward,BA;Boyle,P
通讯作者：
Boyle,P